{"id":"W2401102263","doi":"10.1158/1055-9965.epi-15-1318","title":"A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations","year":2016,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Lunenfeld-Tanenbaum Research Institute","funders":"National Institute of General Medical Sciences; University of Texas MD Anderson Cancer Center; University of Toronto; Centre International de Recherche sur le Cancer; National Cancer Institute; National Institutes of Health; National Science Foundation","keywords":"Genome-wide association study; Lung cancer; Context (archaeology); Receiver operating characteristic; Penetrance; Genetic association; Medicine; Oncology; Cancer; Genetic model; SNP; Biology; Genetics; Internal medicine; Single-nucleotide polymorphism; Genotype","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001515673,0.0009897122,0.0009086299,0.001442115,0.0004431678,0.0008629761,0.001336096,0.0009376995,0.002652125],"category_scores_gemma":[0.004061223,0.0003644578,0.001533335,0.0009437887,0.0003311315,0.0007174877,0.001070223,0.001051911,0.000660693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007183498,"about_ca_system_score_gemma":0.00189723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01258783,"about_ca_topic_score_gemma":0.01483523,"domain_scores_codex":[0.9993068,0.0002301321,0.00004004409,0.0002664825,0.00009181843,0.00006472543],"domain_scores_gemma":[0.9988531,0.0007150288,0.0001137334,0.00009679626,0.0001596683,0.00006173281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001063432,0.0007584515,0.1259889,0.0003056532,0.001538387,0.000601487,0.0002037305,0.6192998,0.009561693,0.003473178,0.003610132,0.2335951],"study_design_scores_gemma":[0.0000705183,0.0001214167,0.005835317,0.0000226568,0.0001669677,0.000180895,0.00001797007,0.9868679,0.0008058886,0.005043813,0.0008413299,0.00002538003],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4509175,0.002325207,0.5351331,0.00160478,0.0001177657,0.0002472219,0.002805655,0.004118932,0.002729813],"genre_scores_gemma":[0.8963726,0.0003602205,0.09890258,0.0003375776,0.00006755664,0.0001465476,0.00203502,0.000126303,0.001651582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01258783,"threshold_uncertainty_score":0.02502906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168048306312613,"score_gpt":0.323857134722542,"score_spread":0.2921766516594159,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}